Modelling conditional probabilities with Riemann-Theta Boltzmann Machines

Carrazza, Stefano, Krefl, Daniel, Papaluca, Andrea

arXiv.org Machine Learning 

The probability density function for the visible sector of a Riemann-Theta Boltzmann machine can be taken conditional on a subset of the visible units. We derive that the corresponding conditional density function is given by a reparameterization of the Riemann-Theta Boltzmann machine modelling the original probability density function. Therefore the conditional densities can be directly inferred from the Riemann-Theta Boltzmann machine.

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